Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis
Qing Wang, Yewang Zhou
Available Online January 2016.
- https://doi.org/10.2991/icaita-16.2016.77How to use a DOI?
- principal component analysis; car ownership; genetic algorithm; neural network
- The prediction of the car ownership is the basic work for city traffic sustainable development. The paper carry on principal component analysis to influencing factors in the process of prediction of Wuhan City car ownership, determine the main components. Combining genetic algorithm with neural network, using genetic algorithm to optimize the weights of neural network, determine the initial weight values of neural network. Not only to improve the neural network training speed and generalization ability, but also overcome that the network is easy to fall into local minimum to a certain extent, then train the neural network, and carry on the prediction of car ownership. At last use a specific example to verify the prediction effect.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Qing Wang AU - Yewang Zhou PY - 2016/01 DA - 2016/01 TI - Genetic Neural Network Prediction of Car Ownership Based on Principal Component Analysis BT - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications PB - Atlantis Press SP - 313 EP - 315 SN - 1951-6851 UR - https://doi.org/10.2991/icaita-16.2016.77 DO - https://doi.org/10.2991/icaita-16.2016.77 ID - Wang2016/01 ER -